השוואת שיטות

	מכונת וקטורים תומכים חסינה	Gradient Boosting חסין (Robust Gradient Boosting)
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2006–2009	2001
הוגה השיטה	Xu, H., Caramanis, C., & Mannor, S.	Friedman, J. H. (with Huber loss from Huber, P. J.)
סוג	Robust supervised classifier / regressor	Ensemble (boosted trees with robust loss)
מקור מכונן	Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510.	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
כינויים	Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees
קשורות	5	6
תקציר	Robust SVM extends the standard support vector machine to resist the influence of outliers and mislabeled points. By replacing the hinge loss with a bounded or non-convex loss function — or by incorporating robust optimization constraints — it learns a decision boundary that is far less distorted by corrupted training examples, making it suitable for noisy real-world datasets where standard SVM would degrade significantly.	Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees.